Event-triggered distributed fixed-time optimization for multi-agent system with input time delays over directed networks

Abstract

This paper develops an event-triggered distributed optimization algorithm (DOA) for directed multi-agent systems. The algorithm is designed to solve fixed-time optimization problems with equality constraints, while also compensating for input time delays. Firstly, the time-delay system is transformed into an equivalent delay-free model by using the method of model order reduction. Secondly, to reduce the consumption of communication resources, an event-triggered communication scheme is utilized. Subsequently, based on Lyapunov stability theory, it is proved that the designed DOA can converge to the optimal solution within a fixed time. Finally, a numerical simulation shows the effectiveness and advantages of the event-triggered DOA for  multi-agent systems.